Post-graduation in Data Science and Decision

Insper

São Paulo, Brazil

2022 - 2021

Bachelor of Science in Computer Science

University of Maryland

College Park, Maryland, USA

2019 - 2014

• Design Cultures & Creativity Honors Program

Data Scientist

MindMiners

São Paulo, Brazil

Current - 2019

• Built interactive dashboards for visualization of data collected by client-made surveys, using Dash, Plotly and Pandas;
• Analyzed app usage metrics and the company’s internal performance data, using SQL and R;
• Created Machine Learning models for text classification and sentiment analysis;

Volunteer Teaching Assistant

Girls Who Code - University of Maryland

College Park, Maryland, USA

2019 - 2018

• Taught high school and middle school girls the fundamentals of programming, using Python;

Data Science: Análise Exploratória de Dados

Insper

São Paulo, Brazil

2020 - 2020

• Studied a framework of data exploration involving analysis, transformation and visualization
• Analyzed and extracted patterns from real world data, contributing to a more efficient decision-making process;
• Created dashboard to present data analysis reports, using data visualization tools;

Data Structures

University of Maryland

College Park, Maryland, USA

2018 - 2018

• Implemented efficient, 1-dimensional data structures, including AVL Trees, Hash Tables and Suffix Arrays;
• Implemented multi-dimensional data structures, including KD-Trees and Quad-Trees;
• Studied the LZW Algorithm for lossless data compression;

Design and Analysis of Computer Algorithms

University of Maryland

College Park, Maryland, USA

2017 - 2017

• Implemented graph and greedy algorithms, including algorithms for bipartiteness, topological sorting, scheduling and minimal spanning trees;
• Studied dynamic programming for problems such as sequence alignment and shortest paths;
• Studied randomized algorithms such as minimum cut and game tree evaluation;

Introduction to Machine Learning

University of Maryland

College Park, Maryland, USA

2017 - 2017

• Implemented different approaches to Machine Learning, including Linear Separators and Neural Networks;
• Studied Principal Component Analysis and probabilistic models, including the Bernoulli Model and Logistic Regression;
• Studied Machine Learning theory, including computational learning theory and PAC efficiency;